The development of several powerful technologies for genome-wide and proteome-wide expression measurements has created an opportunity to study and understand the coordinated activities of large sets of, if not all, an organism's genes in response to a wide variety of conditions and stimuli, e.g. DeRisi et al, Science, 278: 680–686 (1997); Wodicka et al, Nature Biotechnology, 15: 1359–1367 (1997); Velculescu et al, Cell, 243–251 (1997); Brenner et al, Nature Biotechnology, 18: 630–634 (2000); McDonald et al, Disease Markers, 18: 99–105 (2002); Patterson, Bioinformatics, 18 (Suppl 2): S181 (2002). Studies using these technologies have shown that reduced subsets of genes appear to be co-regulated to perform particular functions and that subsets of expressed genes and proteins can be used to classify cells phenotypically, e.g. Shiffman and Porter, Current Opinion in Biotechnology, 11: 598–601 (2000); Afshari et al, Nature, 403: 503–511 (2000); Golub et al, Science, 286: 531–537 (1999); van't Veer et al, Nature, 415: 530–536 (2002); and the like.
An area of interest in drug development is the expression profiles of genes and proteins involved with the metabolism or toxic effects of xenobiotic compounds. Several studies have shown that sets of several tens of genes can serve as indicators of compound toxicity, e.g. Thomas et al, Molecular Pharmacology, 60: 1189–1194 (2001); Waring et al, Toxicology Letters, 120: 359–368 (2001); Longueville et al, Biochem. Pharmacology, 64: 137–149 (2002); and the like. Similarly, in the area of cancer diagnostics and prognosis, the differential expression of sets of a few tens of genes or proteins has been shown frequently to have strong correlations with the progression and prognosis of a cancer.
Accordingly, there is an interest in technologies that provide convenient and accurate measurements of multiple expressed genes in a single assay, either at the messenger RNA level or the protein level, or both. Current approaches to such measurements include multiplexed polymerase chain reaction (PCR), spotted and synthesized DNA microarrays, color-coded microbeads, and single-analyte assays, such as enzyme-linked immunosorbant assays (ELISAs) or Taqman-based PCR, used with robotics apparatus, e.g. Longueville et al (cited above); Elnifro et al, Clinical Microbiology Reviews, 13: 559–570 (2000); Chen et al, Genome Research, 10: 549–557 (2000); and the like. Unfortunately, none of the approaches provides a completely satisfactory solution for the desired measurements for several reasons including difficulty in automating, reagent usage, sensitivity, consistency of results, and so on, e.g. Elnifro et al (cited above); Hess et al, Trends in Biotechnology, 19: 463–468 (2001); King and Sinha, JAMA, 286: 2280–2288 (2001).
In view of the above, the availability of a convenient and cost effective technique for measuring the presence or absence or quantities of multiple analytes, such as gene expression products, in a single assay reaction would advance the art in many fields where such measurements are becoming increasingly important, including life science research, medical research and diagnostics, drug discovery, genetic identification, animal and plant science, and the like.